Journal of Language Evolution最新文献_第3页

Phylogeny of the Turkic Languages Inferred from Basic Vocabulary: Limitations of the Lexicostatistical Methods in an Intensive Contact Situation 从基本词汇推断突厥语言的系统发育:密集接触情况下词汇统计方法的局限性

IF 2.6

Journal of Language Evolution Pub Date : 2022-07-06 DOI: 10.1093/jole/lzac006

Ilya M Egorov, Anna V Dybo, Alexei S Kassian

{"title":"Phylogeny of the Turkic Languages Inferred from Basic Vocabulary: Limitations of the Lexicostatistical Methods in an Intensive Contact Situation","authors":"Ilya M Egorov, Anna V Dybo, Alexei S Kassian","doi":"10.1093/jole/lzac006","DOIUrl":"https://doi.org/10.1093/jole/lzac006","url":null,"abstract":"This article provides an attempt to revise the phylogenetic structure of the Turkic family using a computational lexicostatistical approach. The methodological framework of the present research is characterized by the following features: (1) wordlists with strictly controlled semantics; (2) step-by-step reconstruction using Swadesh wordlists for proto-languages; (3) three stages of post-processing of the input data (analysis of root cognacy, elimination of derivational drift, and optimization of homoplasy); (4) application of several computational algorithms (Starling neighbor-joining, Bayesian MCMC, and maximum parsimony). The analysis provided confirms the status of Chuvash as the first outlier and suggests a subsequent multifurcation of Proto-Nuclear-Turkic into eight branches. The Siberian Turkic group is a purely areal unity, that is, Yakut-Dolgan, Tofa-Tuvinian, Khakas-Mrassu, Sarygh Yugur and Altai do not form a clade. Altai is grouped together with the Kipchak languages as a separate taxon; it does not show a particularly close relationship with Kirghiz, which belongs to another Kipchak subgroup. Karluk is a low-level taxon inside the Kipchak clade.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138519790","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian methods for ancestral state reconstruction in morphosyntax: Exploring the history of argument marking strategies in a large language family 形态句法中祖先状态重建的贝叶斯方法:在一个大语系中探讨论点标记策略的历史

IF 2.6

Journal of Language Evolution Pub Date : 2022-05-28 DOI: 10.1093/jole/lzac002

Joshua L. Phillips, Claire Bowern

引用次数: 2

BayesVarbrul: a unified multidimensional analysis of language change in a speaker community BayesVarbrul：说话人群体语言变化的统一多维分析

IF 2.6

Journal of Language Evolution Pub Date : 2022-04-19 DOI: 10.1093/jole/lzac004

Xia Hua

{"title":"BayesVarbrul: a unified multidimensional analysis of language change in a speaker community","authors":"Xia Hua","doi":"10.1093/jole/lzac004","DOIUrl":"https://doi.org/10.1093/jole/lzac004","url":null,"abstract":"\u0000 Exchange in ideas between language evolution and biological evolution has a long history, due to a shared theoretical foundation between language and biology as two evolving systems. Both systems evolve in terms of the frequency of a variant in a population for each of a large number of variables, that is how often a particular variant of a language variable is used in a speaker community and how many individuals in a biological population carry a particular variant of a gene. The way these frequencies change has been modelled under a similar mathematical framework. Here, I show how we can use concepts from genome wide association studies that identify the source of natural selection and the genes under selection in a biological population to study how social factors affect the usage of language variables in a speaker community or how some social groups use some language variables differently from other groups. Using the Gurindji Kriol language as a case study, I show how this approach unifies existing mathematical and statistical tools in studying language evolution over a large number of speakers and a large number of language variables, which provides a promising link between micro- and macro-evolution in language. The approach is named BayesVarbrul and is ready to apply to datasets other than the Gurindji Kriol dataset, including existing corpus data. The code and the instructions are available at https://github.com/huaxia1985/BayesVarbrul.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-04-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48421279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

The evolution of color naming reflects pressure for efficiency: Evidence from the recent past 颜色命名的演变反映了效率的压力:来自最近的证据

IF 2.6

Journal of Language Evolution Pub Date : 2022-04-11 DOI: 10.1093/jole/lzac001

Noga Zaslavsky, Karee Garvin, Charles Kemp, Naftali Tishby, Terry Regier

引用次数: 0

Methodological Problems in Quantitative Research on Environmental Effects in Phonology 语音学环境效应定量研究的方法论问题

IF 2.6

Journal of Language Evolution Pub Date : 2022-04-09 DOI: 10.1093/jole/lzac003

F. Hartmann

引用次数: 0

A simulation on coevolution between language and multiple cognitive abilities 语言与多种认知能力协同进化的模拟

IF 2.6

Journal of Language Evolution Pub Date : 2022-01-29 DOI: 10.1093/jole/lzab006

T. Gong, L. Shuai, Xiaolong Yang

{"title":"A simulation on coevolution between language and multiple cognitive abilities","authors":"T. Gong, L. Shuai, Xiaolong Yang","doi":"10.1093/jole/lzab006","DOIUrl":"https://doi.org/10.1093/jole/lzab006","url":null,"abstract":"\u0000 We propose a coevolution scenario between language and two cognitive abilities, namely shared intentionality and lexical memory, under a conceptual framework that integrates biological evolution of language learners and cultural evolution of communal language among language users. Piggybacking on a well-attested agent-based model on the origin of simple lexicon and constituent word order out of holistic utterances, we demonstrate: (1) once adopted by early hominins to handle preliminary linguistic materials, along with the origin of an evolving communal language having a high mutual understandability among language users, the initially low levels of the two cognitive abilities are boosted and get ratcheted at sufficiently high levels in language users for proficient language learning and use; (2) the socio-cultural environment is indispensable for the coevolution, and natural selection (selecting highly understandable adults to produce offspring), not cultural selection (choosing highly understandable adults to teach offspring), drives the coevolution. This work modifies existing models and theories of coevolution between language and human cognition and clarifies theoretical controversies regarding the roles of natural and cultural selections on language evolution.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48347890","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Correcting a bias in TIGER rates resulting from high amounts of invariant and singleton cognate sets 纠正由于大量不变和单一同源集而导致的TIGER率偏差

IF 2.6

Journal of Language Evolution Pub Date : 2022-01-19 DOI: 10.1093/jole/lzab007

Johann-Mattis List

{"title":"Correcting a bias in TIGER rates resulting from high amounts of invariant and singleton cognate sets","authors":"Johann-Mattis List","doi":"10.1093/jole/lzab007","DOIUrl":"https://doi.org/10.1093/jole/lzab007","url":null,"abstract":"\u0000 In a recent issue of the Journal of Language Evolution, Syrjänen et al. (2021) investigate the suitability of computing Cummins and McInerney’s (2011) TIGER rates for estimating the tree-likeness of linguistic datasets compiled for phylogenetic reconstruction. The authors test the TIGER rates on a diverse sample of simulated data, which by and large confirms the usefulness of TIGER rates as an analytic tool for investigating linguistic data, but they test them only on one real-world dataset of Uralic languages which turns out to behave quite differently from the simulated data. When testing the TIGER rates on additional datasets, I detected a bias in the computation which leads to an unnatural increase in those cases where a dataset contains many characters with invariant or singleton states. To overcome this problem, I suggest a modified variant of TIGER rates, which is provided in the form of a freely available Python package. Testing the modified TIGER scores on the simulated data of Syrjänen et al. shows that the corrected TIGER rates still readily distinguish between different degrees of tree-likeness. Testing them on a dataset in which the number of singletons and invariants was artificially increased further shows that the corrected TIGER rates are not influenced by the bias. A final tests on seven linguistic datasets show the usefulness of the corrected TIGER rates on a larger variety of linguistic datasets and illustrate the importance to take specific aspects of linguistic data into account when using biological methods in the domain of language evolution.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2022-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43164472","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Crouching TIGER, hidden structure: Exploring the nature of linguistic data using TIGER values 卧虎藏龙，隐藏结构:用TIGER值探索语言数据的本质

IF 2.6

Journal of Language Evolution Pub Date : 2021-11-15 DOI: 10.1093/jole/lzab004

K. Syrjänen, L. Maurits, Unni Leino, T. Honkola, J. Rota, O. Vesakoski

{"title":"Crouching TIGER, hidden structure: Exploring the nature of linguistic data using TIGER values","authors":"K. Syrjänen, L. Maurits, Unni Leino, T. Honkola, J. Rota, O. Vesakoski","doi":"10.1093/jole/lzab004","DOIUrl":"https://doi.org/10.1093/jole/lzab004","url":null,"abstract":"\u0000 In recent years, techniques such as Bayesian inference of phylogeny have become a standard part of the quantitative linguistic toolkit. While these tools successfully model the tree-like component of a linguistic dataset, real-world datasets generally include a combination of tree-like and nontree-like signals. Alongside developing techniques for modeling nontree-like data, an important requirement for future quantitative work is to build a principled understanding of this structural complexity of linguistic datasets. Some techniques exist for exploring the general structure of a linguistic dataset, such as NeighborNets, δ scores, and Q-residuals; however, these methods are not without limitations or drawbacks. In general, the question of what kinds of historical structure a linguistic dataset can contain and how these might be detected or measured remains critically underexplored from an objective, quantitative perspective. In this article, we propose TIGER values, a metric that estimates the internal consistency of a genetic dataset, as an additional metric for assessing how tree-like a linguistic dataset is. We use TIGER values to explore simulated language data ranging from very tree-like to completely unstructured, and also use them to analyze a cognate-coded basic vocabulary dataset of Uralic languages. As a point of comparison for the TIGER values, we also explore the same data using δ scores, Q-residuals, and NeighborNets. Our results suggest that TIGER values are capable of both ranking tree-like datasets according to their degree of treelikeness, as well as distinguishing datasets with tree-like structure from datasets with a nontree-like structure. Consequently, we argue that TIGER values serve as a useful metric for measuring the historical heterogeneity of datasets. Our results also highlight the complexities in measuring treelikeness from linguistic data, and how the metrics approach this question from different perspectives.","PeriodicalId":37118,"journal":{"name":"Journal of Language Evolution","volume":null,"pages":null},"PeriodicalIF":2.6,"publicationDate":"2021-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46292851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

The philosophical interpretation of language game theory 语言博弈论的哲学解释

IF 2.6

Journal of Language Evolution Pub Date : 2021-11-03 DOI: 10.1093/jole/lzab003

Nick Zangwill

引用次数: 1

Bayesian phylogenetic analysis of linguistic data using BEAST 基于BEAST的语言数据贝叶斯系统发育分析

IF 2.6

Journal of Language Evolution Pub Date : 2021-09-23 DOI: 10.1093/jole/lzab005

Konstantin Hoffmann, R. Bouckaert, Simon J. Greenhill, D. Kühnert

引用次数: 10